There is a wide range of cases where an organization needs to populate its information system (e.g. a relational database) using the field values presented on a paper document of a given type. Paper documents may be fixed layout forms, invoices, lockbox statements, explanation-of-benefits (“EOB”) documents, deeds of trust, etc. The structure of the database that is to be populated is a data model. Systems may be built that may automatically locate various fields of input document pages, recognize them and match them to items of the data model. In most cases, those systems may be able to process only a portion of the task, the remainder being performed manually by keying operators. However, in some cases, a very small portion or none of the fields may be automatically matched to their corresponding items inside the data model.
Thus, there is a need to increase the efficiency of the keying operator performing manual feeding of information system by allowing him/her to interact dynamically with the recognition engine. In this semi-automatic recognition process, the operator may perform partially the location task, while the recognition engine may perform the remainder of the location task, and the recognition task. There is further a need for a more efficient image processing and object recognition that may be capable of using partial information that may be available with regard to the document/object being processed and providing that information to the recognition engine to perform the recognition task. There is further a need for a more efficient image processing and character/object recognition that may be capable of performing recognition of handwritten characters/objects and typewritten/machine-written characters/objects alike.